Runs ordinal logistic regression models and produces DIF statistics and effect size measures
Usage
runolr(rv, ev, gr)
Arguments
rv
a response variable
ev
an explanatory variable (e.g., conditioning variable)
gr
a vector of group identifiers
Value
Returns a list of the following components:
chi12prob for the LR Chi-square comparing Model 1 vs. Model 2
chi13prob for the LR Chi-square comparing Model 1 vs. Model 3
chi23prob for the LR Chi-square comparing Model 2 vs. Model 3
beta12proportional change in the coefficient for ev
pseudo1.CoxSnellCox & Snell psudo R-square for Model 1
pseudo2.CoxSnellCox & Snell psudo R-square for Model 2
pseudo3.CoxSnellCox & Snell psudo R-square for Model 1
pseudo1.NagelkerkeNagelkerke psudo R-square for Model 1
pseudo2.NagelkerkeNagelkerke psudo R-square for Model 2
pseudo3.NagelkerkeNagelkerke psudo R-square for Model 3
pseudo1.McFaddenMcFadden psudo R-square for Model 1
pseudo2.McFaddenMcFadden psudo R-square for Model 2
pseudo3.McFaddenMcFadden psudo R-square for Model 3
pseudo12.CoxSnellCox & Snell R-square change from Model 1 to Model 2
pseudo13.CoxSnellCox & Snell R-square change from Model 1 to Model 3
pseudo23.CoxSnellCox & Snell R-square change from Model 2 to Model 3
pseudo12.NagelkerkeNagelkerke R-square change from Model 1 to Model 2
pseudo13.NagelkerkeNagelkerke R-square change from Model 1 to Model 3
pseudo23.NagelkerkeNagelkerke R-square change from Model 2 to Model 3
pseudo12.McFaddenMcFadden R-square change from Model 1 to Model 2
pseudo13.McFaddenMcFadden R-square change from Model 1 to Model 3
pseudo23.McFaddenMcFadden R-square change from Model 2 to Model 3
df12df for the LR Chi-square comparing Model 1 and Model 2
df13df for the LR Chi-square comparing Model 1 and Model 3
df23df for the LR Chi-square comparing Model 2 and Model 3
Details
Model 1: ev
Model 2: ev + gr
Model 3: ev*gr or equivalently ev + gr + ev*gr
References
Choi, S. W., Gibbons, L. E., Crane, P. K. (2011). lordif: An R Package for Detecting Differential Item Functioning Using Iterative Hybrid Ordinal Logistic Regression/Item Response Theory and Monte Carlo Simulations. Journal of Statistical Software, 39(8), 1-30. URL http://www.jstatsoft.org/v39/i08/.
Crane, P. K., Gibbons, L. E., Jolley, L., & van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques: DIF detect and difwithpar. Medical Care, 44(11 Suppl 3), S115-S123.